Motivation.
Although some papers illustrate large-scale GCN experiments [1]}, [2]}, the models (and data) used in GCN research remain small in comparison to main-stream deep learning research [3]}, [4]}.
Formative publications in the area even hinted at the challenge of training larger (deeper) models [4]}.
In contrast, recent deep learning research has revealed that severely overparameterized models can discover generalizable solutions [6]}, creating a trend toward larger models and datasets [7]}, [8]}.
These findings motivate the exploration of larger models and datasets for machine learning on graphs.
